Trailblazing Mavericks Required

November
17th,
2014

Recently, I’ve noticed a pattern emerging of papers being published that show that ‘simplistic methods’ actually work - and are competitive with the ground-breakingly good results from the NN deep learning community. For example :

The Need for Trailblazers

To stereotype the implementers of the ‘new simple’ methods : They are practicing ‘white magic’, using tried and true methods, that almost self-evidently work (except that no-one had thought to try them before).

Conversely, the methods now shown to have over-complicated the problem, are ‘black magic’. And the wizards who create these methods are (essentially) willing to try anything that will give interesting results. Typically, the people performing ‘black magic’ are Neural Network researchers, who are accustomed to searching in the wilderness, trying to defend a field that seemed a little hopeless.

Recently, though, people who had previously been out on the bold frontier of research have recently been emboldened by the sudden computational tractability of their work (before the early-2000s, stochastic gradient descent was an almost thankless optimisation method). But now, suddenly, having any kind of differentiable system (even Neural Turing Machines…) means that a problem can be tackled by a sufficient number of CPUs (or, more likely) GPUs. It almost seems like a reasonable approach…

Back to the “Trailblazers” : These are the ‘have-a-go’ black magic wizards. Once the problems have been shown to be tractable at all, then the white magic wizards can come along and demonstrate that it was ‘simple’ all along. But one needs the Trailblazers, the ‘dreamers’ - for otherwise others will fear to venture into the unknown.